Decision-Tree is a classic non-parametric machine learning algorithm. A tree is created through learning decision rules inferred from data features. Starting from the top root node, data are split into different internal nodes according to certain cutoff values in features, and then finally arrive the terminal leaf nodes which give the final classification result. ID3 [1]} and CART [2]} are classic Decision-Tree algorithms which employ Information Gain and Gini Impurity from Entropy Theory [3]} as measurements in making best splitting rules.
